Network management becomes more and more complicated as well as the requirements or expectations on network operators that they should be able to offer user-centric end-to-end always-best connectivity. A complicating factor is that the networks increasingly become more heterogeneous and complex. Future networks will basically be service-driven and the user will expect constant service availability on any network to which the user has access. These networks will consist of a large variety of different access and core networks and be required to offer many services simultaneously. They will also exhibit considerably more dynamic behaviour than current networks do in order to be able to quickly, practically in real-time, adapt to end user needs for best quality of experience (QoE) as well as operator needs for optimal resource management at a reasonable operator expenditure (OPEX).
This complexity of the communication network will in turn require complex, distributed and preferably, to a large extent, self-adaptive network management systems. There will thus be a need for network management techniques supporting distributed autonomous self-managed networks. Therefore policy-based network management has been suggested which attempts to deploy expert knowledge in the network about services, interaction between services, user preferences and strategic views of businesses to allow the network to make decisions on how to manage these services in a dynamic, heterogeneous multi-service environment.
In for example policy driven, distributed and self-managed networks the devices of the network exhibit individual behaviour in order to fulfill a service and/or user requirements. Such an individual behaviour will affect the network as a whole. Therefore, to be able to observe the behaviour of the network becomes critical for purposes such as forecasting and detection of undesired behaviour, malfunctioning etc.
In order to be able to monitor the behaviour of the network and/or network devices, the management system must monitor events relevant to the network device, i.e. what it is doing, and the status of the network device, i.e. how it is doing. For conceptual monitoring of behaviour, it should be possible to describe the entity, for example a service and its features to be monitored and to build a model that observes the entity. “Tutorial ontological engineering—Part 1: Introductions to Ontological Engineering”, 2003 by R. Mizoguchi, describes a way to represent knowledge based on ontological engineering. Ontological engineering focuses on how concepts can be captured in a given domain with the purpose of sharing a common understanding of this domain and to thus enable interoperability and knowledge reuse. Ontologies are taxonomies of concepts and their attributes in a given domain together with a formal representation of domain assumptions. Formal here means that it is semantically rich and based on a well-understood logical paradigm such as Description Logics, c.f. “Description Logics”, Handbook on Ontologies, Springer, Ed., 2004 by F. Baader et al. or Frame Logics, c.f. “Ontologies in F-logic”, in Handbook on Ontologies: Springer, 2004, by J. Angele and G. Lausan. Formal ontologies are thus based on well-defined semantics enabling machine-readability and reasoning about information through various inference (reasoning) capabilities being supported, c.f. “d20.2v02: OWL—Lite reasoning with rules”, 2004 by A. Harth and S. Decker.
Probabilistic graphical models provide a means to monitor behaviour by specifying the dependencies (and independencies) that hold between aspects of a system. So called Bayesian networks (BN), as e.g. discussed in “Bayesian Networks and Decision graphs”, New York: Springer-Verlag, 2001, by F. V. Jensen, are a subset of probabilistic graphical models based on directed acyclic graphs. BNs are currently used to monitor different types of behaviour, for example power consumption or fault propagation. A BN consists of a graphical structure, where nodes represent statistical variables from an application domain and arcs the influential relationships between them, as well as an associated numerical part, encoding the conditional probability distribution over these variables. The conditional probability distribution encodes the probability that the variables assume their different values given the values of other variables in the BN.
The purpose of employing ontological representations is to capture concepts in a given domain in order to provide a shared common understanding of said domain, enabling interoperability and knowledge reuse, but also machine-readability and reasoning about information through inferencing. Ontological representations are deterministic and consist of concepts and facts about domains and their relationships to each other.
The purpose of Bayesian networks is to provide a means for estimating complex probabilities of states based on graphical models of a domain. They also provide a structured representation of knowledge and specify the relationship between concepts (or variables) of a domain. BNs are probabilistic, encoding the probability that variables assume particular values given a value of their parent variables in the BN structure.
Ontologies and Bayesian Networks have independently been used to facilitate machine reasoning and decision making.
BNs have predominantly been applied for fault management purposes.
The task of building a Bayesian Network is extremely complex and knowledge-intensive. It requires identification of relevant statistical variables in the application domain, specification of causality relations between these variables and assignment of initial probabilities for the numerical part of the BNs. The BN structure may be defined by hand or derived from data. Building BNs by hand requires a lot of human expert knowledge in the application domain, is extremely laborious and cannot be automated. On the other hand, deriving BNs from data requires enormous amounts of data and the BN structure must then be validated by human evaluators or with reference to human-annotated data.
US 2004/0153429 describes an approach for automatical creation of causal networks (Bayesian network). However, it relies on a well-defined data structure directly relevant to the BN generation that a user can fill in and then the generation of the BN is done automatically by some piece of software. The data structure is not generic and cannot be reused for other purposes. Thus, it is not flexible and its applicability is very limited.
Thus, although it has been realized that it would be attractive to be able to use probabilistic, causal networks, for example Bayesian Networks, within network management their use has practically been limited to fault management due to the extremely complex and knowledge intensive task of building a Bayesian Network. Further it is neither realistic to build BNs by hand, i.e. to derive graph structures manually, which is very complex, requiring domain as well as BN knowledge, nor to rely on well-defined data structures which would result in rigid, inflexible systems.